Identifying femicide locally and globally: Understanding the utility and accessibility of sex/gender-related motives and indicators
Bibliographic record
Abstract
Femicide, the gender-related killing of women and girls, has received an unprecedented rise in international attention in the past decade, prompting increased discussions about how to define and measure femicide. Following a review of definitions and indicators, this article examines the utility of numerous sex/gender-related motives and indicators (SGRMIs) for distinguishing femicide from other homicides as well as the accessibility of these indicators in data sources typically accessed by social science researchers. Specifically, using a comprehensive database whose primary focus is femicide, the presence of SGRMIs in male-perpetrator/female-victim homicide – those killings most closely aligned with the concept of femicide – is compared to other perpetrator–victim gender combinations. Results show that multiple SGRMIs are more common in male-perpetrator/female-victim killings than other homicides, meaning they are useful for distinguishing femicide as a distinct type of violence. However, accessibility to information is weak with high proportions of missing data. Implications of these findings for prevention are discussed, including how data biases may be putting the lives of women and girls at risk and the need to emphasize prevention as the priority for data collection rather than administrative needs of governments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".